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import torch
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def calculate_evaluation_metrics(mask_true, mask_pred):
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    intersection = torch.logical_and(mask_true, mask_pred).float().sum()
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    union = torch.logical_or(mask_true, mask_pred).float().sum()
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    iou = intersection / union
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    true_positive = torch.logical_and(mask_true, mask_pred).float().sum()
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    false_positive = torch.logical_and(~mask_true, mask_pred).float().sum()
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    false_negative = torch.logical_and(mask_true, ~mask_pred).float().sum()
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    precision = true_positive / (true_positive + false_positive)
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    recall = true_positive / (true_positive + false_negative)
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    f1_score = 2 * (precision * recall) / (precision + recall)
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    accuracy = (true_positive + torch.logical_and(~mask_true, ~mask_pred).float().sum()) / mask_true.numel()
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    metrics = {
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        'accuracy': accuracy.item(),
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        'iou': iou.item(),
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        'precision': precision.item(),
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        'recall': recall.item(),
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        'f1_score': f1_score.item()
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    }
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    return metrics